About R
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. …… R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
R-4.5.1 was released since 2025 June.
How/where to obtain and install R?
If you use linux as your default os, you can install R from the package repositories of each distribution directly. Alternatively, you can download R binary-version or source code from CRAN if you use M$ windows or Mac OS.
Ubuntu users
- Update indices with
sudo apt update -qq - Install two helper packages we need
sudo apt install --no-install-recommends software-properties-common dirmngr - Add the signing key with
wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc - Add the repo from CRAN with
sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" - Install R with
sudo apt install --no-install-recommends r-base r-base-dev
MacOS users
- Intel x86-64: R-4.5.1-x86_64.pkg
- Apple silicon arm64: R-4.5.1-arm64.pkg
- To compile R packages which contain Fortran code, Xcode 16.2 and arm64-version GNU Fortran14.2 is required.
- XQuartz >= 2.8.5 is
required to use X11 (including
tcltk).
Windows users
- Download the latest version of R from CRAN
- Install R with the downloaded installer
- Install Rtools from CRAN
- Rtools is a collection of tools for building R packages on Windows. It includes a compiler, a set of libraries, and other tools that are needed to build R packages from source.
- Add Rtools to your PATH environment variable
- Open the Control Panel and go to System and Security > System > Advanced system settings > Environment Variables.
- Under System variables, find the PATH variable and click Edit.
- Add the path to the Rtools bin directory (e.g.,
C:\Rtools\bin) to the PATH variable. - Click OK to save the changes.
Using RStudio as your default R-programming IDE
About RStudio
RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
Install the most suitable version of RStudio for your needs.
- Desktop version: Access RStudio locally.
- Server version: Access via a web browser.
: 
: Resources of learning R
1. Books & web sites
2. Forums & communities
The most important step when beginning to learn R is using help()
help() & help.search()
help(help)
help.search("standard deviation")? & ??
?mean
??hypergeometricPackage installation & PATH setting
Installing packages in R console
# Download Pkgs from CRAN repository & install
install.packages('rmarkdown', # Package name
repo="http://cran.csie.ntu.edu.tw", # The URL of CRAN repository
destdir="~/Download", # The directory where downloaded pkgs are stored
lib=.libPaths()[1]) # The directory where to install pkgs
# Install Pkgs from downloaded source code
install.packages('~/Download/rmarkdown_0.5.1.tar.gz',
repos=NULL,
type="source",
lib=.libPaths()[1])Installing packages in terminal
$ R CMD INSTALL -l $HOME/R/4.1 rmarkdown_0.5.1.tar.gzSetting PATH
.libPaths(new) # .libPaths("/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library")Some Pkgs should be downloaded/installed from R-forge
Set
install.packages(Pkg, repo='http://R-Forge.R-project.org')
Using the package installed
library(Pkg)
require(Pkg) # Avoid to use this!What is the difference between require() and library()
Bioconductor
About Bioconductor
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development.
Install Pkgs from Bioconductor
# Install BiocManager
install.packages("BiocManager")
BiocManager::install(pkgname)
Basic operation
5+5
5-3
5*3
5/3
5^3
10%%3
# Variable declaration
x <- 5 # '<-' is assign operator in R, which is equivalent to '='
y <- function(i) mean(i)Data and object types
Data types
- numeric:
c(1:3, 5 ,7) - character:
c("1","2","3"); LETTERS[1:3] - logical:
TRUE; FALSE - complex: 1, b, 3
Object types
- vector: the data types of all elements in a vector must be consistent!
x <- 1:5
y <- c(6,7,8,9,10)
z <- x - y
print(z)## [1] -5 -5 -5 -5 -5# Vectorized code performs better!
a <- 1:100000
system.time(mean(a))## user system elapsed
## 0 0 0total <- 0
system.time(for (i in a) {total <- total + i; total/100000})## user system elapsed
## 0.002 0.000 0.002- matrix
x <- matrix(rnorm(100), nr=20, nc=5)
print(x)## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.55038124 0.184262481 -0.4561954 1.14895276 -1.92047276
## [2,] -1.58578387 -0.007294847 -0.1266443 0.65476618 1.48662616
## [3,] -0.94487570 -0.256090246 1.7646225 2.37595500 0.79648732
## [4,] -1.25740801 1.174664052 -0.2192437 -0.57965671 -1.87931963
## [5,] 1.21101090 -0.250846972 0.6736424 0.06751027 0.15817507
## [6,] 0.10050912 0.724036994 0.4626070 -2.18041770 -0.47613009
## [7,] -1.67498300 -0.108837647 -1.1641596 1.40305109 -0.57147186
## [8,] -1.65563785 -1.109198549 0.3337019 0.45362140 0.40031438
## [9,] -0.31053419 -2.128176652 0.1197364 -0.87086882 -0.01925781
## [10,] 0.54898811 -2.074307611 1.7757706 -0.60428193 1.39690828
## [11,] -1.58184057 0.036519672 1.1332032 1.18419250 -0.36168788
## [12,] 0.77839776 1.624411608 0.5920319 1.71761575 1.45908984
## [13,] 1.31375889 0.500091929 1.1144533 -1.75353085 -0.32319226
## [14,] 0.05202539 0.249442979 -1.3079697 -3.17371927 0.08209465
## [15,] 1.05060515 1.758710198 0.3199155 -1.45387452 0.02531780
## [16,] 1.85035452 -1.019057568 0.6645884 0.63302917 -1.43927096
## [17,] 0.26549603 -0.637796473 -1.0054882 0.88026645 0.52535503
## [18,] -0.20088955 -0.992080587 -0.3495000 -1.15589046 0.09926207
## [19,] 0.21709471 -0.802507844 1.7528800 -0.26107060 -1.14192450
## [20,] -1.45922886 0.428763413 1.2400165 0.43486224 -1.81211307x[1,3]
x[2:4,]
x[,3:5]
x %*% t(x)
# A matrix is a vector with subscripts!
x[1:3]
x[1:3,1]- array
y <- array(rnorm(64), c(8,4,2))
print(y) # An array is also a vector with subscripts!## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] -0.24242102 0.2933525 0.2349756 0.43253848
## [2,] -1.28912989 1.2934986 1.8828807 0.05210916
## [3,] 0.10792740 0.6808473 0.7594639 0.34553260
## [4,] -0.42420471 0.3020578 0.1506608 -0.78573316
## [5,] -1.52540753 -1.1579384 -0.2453179 0.95768947
## [6,] 1.16660007 -0.3250908 -0.4550057 0.44779420
## [7,] 0.40456494 0.4160561 -0.5125978 -0.70044813
## [8,] 0.08019048 -0.1122778 -1.7348882 0.21477218
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 0.56596731 -1.62993185 -0.03481932 1.4169964
## [2,] 0.19394806 0.74626495 1.62493451 -0.6921677
## [3,] -0.06247529 -1.01941115 1.30076147 -1.3304937
## [4,] 0.69862984 -1.23584830 -0.65781036 1.1487815
## [5,] 0.12240414 -0.67416104 0.70132428 -0.1913586
## [6,] 1.41164163 -0.04719757 -2.08769008 -1.5000244
## [7,] -0.50737293 1.06244430 -0.25884372 -1.4470308
## [8,] -0.22431286 -1.15206621 1.12773303 -0.9611897- list: the data types of elements in a list could be complex
x<-list(1:5, c("a","b","c"), matrix(rnorm(10),nr=5,nc=2))
print(x)## [[1]]
## [1] 1 2 3 4 5
##
## [[2]]
## [1] "a" "b" "c"
##
## [[3]]
## [,1] [,2]
## [1,] 0.1659226 0.1472247
## [2,] 0.5536150 -1.8438265
## [3,] -0.7093551 0.6275619
## [4,] -0.1035621 -0.4417174
## [5,] 1.2053090 -0.8757276x$mylist <- x
print(x)## [[1]]
## [1] 1 2 3 4 5
##
## [[2]]
## [1] "a" "b" "c"
##
## [[3]]
## [,1] [,2]
## [1,] 0.1659226 0.1472247
## [2,] 0.5536150 -1.8438265
## [3,] -0.7093551 0.6275619
## [4,] -0.1035621 -0.4417174
## [5,] 1.2053090 -0.8757276
##
## $mylist
## $mylist[[1]]
## [1] 1 2 3 4 5
##
## $mylist[[2]]
## [1] "a" "b" "c"
##
## $mylist[[3]]
## [,1] [,2]
## [1,] 0.1659226 0.1472247
## [2,] 0.5536150 -1.8438265
## [3,] -0.7093551 0.6275619
## [4,] -0.1035621 -0.4417174
## [5,] 1.2053090 -0.8757276- data frame: a data frame is collection of multiple lists with the same length
df<-data.frame(num=1:10,
char=LETTERS[1:10],
logic=sample(c(TRUE,FALSE), 10, replace=TRUE))
df## num char logic
## 1 1 A TRUE
## 2 2 B FALSE
## 3 3 C TRUE
## 4 4 D FALSE
## 5 5 E FALSE
## 6 6 F FALSE
## 7 7 G TRUE
## 8 8 H FALSE
## 9 9 I FALSE
## 10 10 J FALSEdf$char## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"df$logic[5:7]## [1] FALSE FALSE TRUE- factor: An R factor might be viewed simply as a vector with a bit more information added (though, as seen below, it’s different from this internally). That extra information consists of a record of the distinct values in that vector, called levels.
x <- c(5, 12, 32, 12)
xf <- factor(x)
print(xf)## [1] 5 12 32 12
## Levels: 5 12 32So…. a factor looks like a vector, right?
str(xf) # Here str stands for structure. This function shows the internal structure of any R object.## Factor w/ 3 levels "5","12","32": 1 2 3 2unclass(xf)## [1] 1 2 3 2
## attr(,"levels")
## [1] "5" "12" "32"length(xf)## [1] 4What??? What are you talking about?
x <- c(5, 12, 13, 12)
xff <- factor(x, levels=c(5, 12, 13, 88))
xff## [1] 5 12 13 12
## Levels: 5 12 13 88xff[2] <- 88
xff## [1] 5 88 13 12
## Levels: 5 12 13 88xff[2] <- 28 # You cannot sneak in an "illegal" level## Warning in `[<-.factor`(`*tmp*`, 2, value = 28): invalid factor level, NA
## generated- table: Another common way to store information is in a table.
# One way table
a <- factor(c("A","A","B","A","B","B","C","A","C"))
a## [1] A A B A B B C A C
## Levels: A B Ca.table <- table(a)
a.table## a
## A B C
## 4 3 2attributes(a.table)## $dim
## [1] 3
##
## $dimnames
## $dimnames$a
## [1] "A" "B" "C"
##
##
## $class
## [1] "table"# Two way table
a <- c("Sometimes","Sometimes","Never","Always","Always","Sometimes","Sometimes","Never")
b <- c("Maybe","Maybe","Yes","Maybe","Maybe","No","Yes","No")
twoway.table <- table(a,b)
twoway.table## b
## a Maybe No Yes
## Always 2 0 0
## Never 0 1 1
## Sometimes 2 1 1# An example
sexsmoke<-matrix(c(70,120,65,140),ncol=2,byrow=TRUE)
rownames(sexsmoke)<-c("male","female")
colnames(sexsmoke)<-c("smoke","nosmoke")
sexsmoke <- as.table(sexsmoke)
sexsmoke## smoke nosmoke
## male 70 120
## female 65 140Control structures
Conditional excutions
- equal: ==
- not equal: !=
- greater/less than: >, <
- greater/less than or equal: >=, <=
Logical operators
- and: &, &&
- or: |, ||
- not: !
if-else statements
if (cond1==TRUE) {cmd1} else {cmd2}# Example
if (1 == 0) {
print(1)
} else {
print(2)
}## [1] 2ifelse statements (ternary operator in R)
ifelse(test, true_value, false_value)x <- 1:10
ifelse(x<5|x>8, x, 0)## [1] 1 2 3 4 0 0 0 0 9 10switch-case statements
AA <- 'foo'
switch(AA,
foo = {print('AA is foo')},
bar = {print('AA is bar')},
{print('Default')}
)## [1] "AA is foo"Loops
For loop
for (var in vector) {
statement
}# Example
mydf <- iris
head(mydf)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosamyve <- NULL
for (i in 1:nrow(mydf)) {
myve <- c(myve, mean(as.numeric(mydf[i, 1:3])))
}
myve## [1] 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000
## [9] 2.900000 3.166667 3.533333 3.266667 3.066667 2.800000 3.666667 3.866667
## [17] 3.533333 3.333333 3.733333 3.466667 3.500000 3.433333 3.066667 3.366667
## [25] 3.366667 3.200000 3.333333 3.400000 3.333333 3.166667 3.166667 3.433333
## [33] 3.600000 3.700000 3.166667 3.133333 3.433333 3.300000 2.900000 3.333333
## [41] 3.266667 2.700000 2.966667 3.366667 3.600000 3.066667 3.500000 3.066667
## [49] 3.500000 3.233333 4.966667 4.700000 4.966667 3.933333 4.633333 4.333333
## [57] 4.766667 3.533333 4.700000 3.933333 3.500000 4.366667 4.066667 4.566667
## [65] 4.033333 4.733333 4.366667 4.200000 4.300000 4.000000 4.633333 4.300000
## [73] 4.566667 4.533333 4.533333 4.666667 4.800000 4.900000 4.466667 3.933333
## [81] 3.900000 3.866667 4.133333 4.600000 4.300000 4.633333 4.833333 4.333333
## [89] 4.233333 4.000000 4.166667 4.566667 4.133333 3.533333 4.166667 4.300000
## [97] 4.266667 4.466667 3.533333 4.200000 5.200000 4.533333 5.333333 4.933333
## [105] 5.100000 5.733333 3.966667 5.500000 5.000000 5.633333 4.933333 4.800000
## [113] 5.100000 4.400000 4.566667 4.966667 5.000000 6.066667 5.733333 4.400000
## [121] 5.266667 4.433333 5.733333 4.633333 5.233333 5.466667 4.600000 4.666667
## [129] 4.933333 5.333333 5.433333 6.033333 4.933333 4.733333 4.766667 5.600000
## [137] 5.100000 5.000000 4.600000 5.133333 5.133333 5.033333 4.533333 5.300000
## [145] 5.233333 4.966667 4.600000 4.900000 5.000000 4.666667while loop
while (condition) statements# Example
z <- 0
while (z < 5) {
z <- z + 2
print(z)
}## [1] 2
## [1] 4
## [1] 6apply loop
For matrix/array
apply(X, MARGIN, FUN, ARGS)
# Examples
apply(iris[,1:3], 1, mean)
x <- 1:10
apply(as.matrix(x), 1, function(i) {
if (i < 5)
i - 1
else
i/i
})For vector/list
lapply(X, FUN)
sapply(X, FUN)# Examples
mylist <- as.list(iris[1:3, 1:3])
mylist## $Sepal.Length
## [1] 5.1 4.9 4.7
##
## $Sepal.Width
## [1] 3.5 3.0 3.2
##
## $Petal.Length
## [1] 1.4 1.4 1.3lapply(mylist, sum) # Compute sum of each list component and return result as list## $Sepal.Length
## [1] 14.7
##
## $Sepal.Width
## [1] 9.7
##
## $Petal.Length
## [1] 4.1sapply(mylist, sum) # Compute sum of each list component and return result as vector## Sepal.Length Sepal.Width Petal.Length
## 14.7 9.7 4.1More apply functions
- tapply
- mapply
function
FunctionName <- function(arg1, arg2, ...) {
statements
return(R_object)
}add <- function(a, b) {
c <- a + b
return(c)
}
x <- 5
y <- 7
z <- add(x,y)
z## [1] 12Advanced R programming
Garbage collection
- rm()
- gc()
x <- as.matrix(read.table("test.csv", sep="\t")) # x is a 4500000 x 220 matrix
y <- apply(x, 1, mean)
rm(list=c("x","y"))
gc()Use data.table to speed up acquisition of data
See Introduction to the data.table package in R
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development. - from CRAN
library(data.table)
grpsize <- ceiling(1e7/26^2)
DF <- data.frame(
x=rep(LETTERS, each=26*grpsize),
y=rep(letters, each=grpsize),
v=runif(grpsize*26^2),
stringsAsFactors=FALSE)
system.time(ans1 <- DF[DF$x=="R" & DF$y=="h",])## user system elapsed
## 0.058 0.009 0.066DT <- as.data.table(DF)
setkey(DT, x, y)
system.time(ans2 <- DT[list("R","h")])## user system elapsed
## 0.014 0.001 0.004Tidyverse
The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying philosophy and common APIs.
Hadley Wickham
install.packages("tidyverse")- magrittr > A Forward-Pipe Operator for R
Use this equation as an example:
\[ \LARGE \boldsymbol{log(\sum_{i=1}^{n}exp(x_i))} \]
In R, you may want to calculate the equation with many functions like this:
log(sum(exp(MyData)), exp(1))With magrittr, you can calculate the equation like
this:
MyData %>% exp %>% sum %>% log(exp(1))- plyr
“ plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together. It’s already possible to do this with split and the apply functions, but plyr just makes it all a bit easier. . . ”
set.seed(1)
d <- data.frame(year = rep(2000:2005, each=3),
count = round(runif(runif(18, 0, 20)))
)
print(d)## year count
## 1 2000 0
## 2 2000 1
## 3 2000 1
## 4 2001 0
## 5 2001 1
## 6 2001 0
## 7 2002 0
## 8 2002 0
## 9 2002 0
## 10 2003 0
## 11 2003 1
## 12 2003 0
## 13 2004 0
## 14 2004 1
## 15 2004 0
## 16 2005 0
## 17 2005 1
## 18 2005 1library(plyr)
ddply(d, "year", function(x) {
mean.count <- mean(x$count)
sd.count <- sd(x$count)
cv <- sd.count/mean.count
data.frame(cv.count=cv)
})## year cv.count
## 1 2000 0.8660254
## 2 2001 1.7320508
## 3 2002 NaN
## 4 2003 1.7320508
## 5 2004 1.7320508
## 6 2005 0.8660254dplyr > dplyr is a package for data manipulation, written and maintained by Hadley Wickham. It provides some great, easy-to-use functions that are very handy when performing exploratory data analysis and manipulation.
filter(): the function will return all the rows that satisfy a following condition.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Let's start with a dataset about air quality
head(airquality)## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6# Filter the records with Temp <= 70
filter(airquality, Temp > 70)## Ozone Solar.R Wind Temp Month Day
## 1 36 118 8.0 72 5 2
## 2 12 149 12.6 74 5 3
## 3 7 NA 6.9 74 5 11
## 4 11 320 16.6 73 5 22
## 5 45 252 14.9 81 5 29
## 6 115 223 5.7 79 5 30
## 7 37 279 7.4 76 5 31
## 8 NA 286 8.6 78 6 1
## 9 NA 287 9.7 74 6 2
## 10 NA 186 9.2 84 6 4
## 11 NA 220 8.6 85 6 5
## 12 NA 264 14.3 79 6 6
## 13 29 127 9.7 82 6 7
## 14 NA 273 6.9 87 6 8
## 15 71 291 13.8 90 6 9
## 16 39 323 11.5 87 6 10
## 17 NA 259 10.9 93 6 11
## 18 NA 250 9.2 92 6 12
## 19 23 148 8.0 82 6 13
## 20 NA 332 13.8 80 6 14
## 21 NA 322 11.5 79 6 15
## 22 21 191 14.9 77 6 16
## 23 37 284 20.7 72 6 17
## 24 12 120 11.5 73 6 19
## 25 13 137 10.3 76 6 20
## 26 NA 150 6.3 77 6 21
## 27 NA 59 1.7 76 6 22
## 28 NA 91 4.6 76 6 23
## 29 NA 250 6.3 76 6 24
## 30 NA 135 8.0 75 6 25
## 31 NA 127 8.0 78 6 26
## 32 NA 47 10.3 73 6 27
## 33 NA 98 11.5 80 6 28
## 34 NA 31 14.9 77 6 29
## 35 NA 138 8.0 83 6 30
## 36 135 269 4.1 84 7 1
## 37 49 248 9.2 85 7 2
## 38 32 236 9.2 81 7 3
## 39 NA 101 10.9 84 7 4
## 40 64 175 4.6 83 7 5
## 41 40 314 10.9 83 7 6
## 42 77 276 5.1 88 7 7
## 43 97 267 6.3 92 7 8
## 44 97 272 5.7 92 7 9
## 45 85 175 7.4 89 7 10
## 46 NA 139 8.6 82 7 11
## 47 10 264 14.3 73 7 12
## 48 27 175 14.9 81 7 13
## 49 NA 291 14.9 91 7 14
## 50 7 48 14.3 80 7 15
## 51 48 260 6.9 81 7 16
## 52 35 274 10.3 82 7 17
## 53 61 285 6.3 84 7 18
## 54 79 187 5.1 87 7 19
## 55 63 220 11.5 85 7 20
## 56 16 7 6.9 74 7 21
## 57 NA 258 9.7 81 7 22
## 58 NA 295 11.5 82 7 23
## 59 80 294 8.6 86 7 24
## 60 108 223 8.0 85 7 25
## 61 20 81 8.6 82 7 26
## 62 52 82 12.0 86 7 27
## 63 82 213 7.4 88 7 28
## 64 50 275 7.4 86 7 29
## 65 64 253 7.4 83 7 30
## 66 59 254 9.2 81 7 31
## 67 39 83 6.9 81 8 1
## 68 9 24 13.8 81 8 2
## 69 16 77 7.4 82 8 3
## 70 78 NA 6.9 86 8 4
## 71 35 NA 7.4 85 8 5
## 72 66 NA 4.6 87 8 6
## 73 122 255 4.0 89 8 7
## 74 89 229 10.3 90 8 8
## 75 110 207 8.0 90 8 9
## 76 NA 222 8.6 92 8 10
## 77 NA 137 11.5 86 8 11
## 78 44 192 11.5 86 8 12
## 79 28 273 11.5 82 8 13
## 80 65 157 9.7 80 8 14
## 81 NA 64 11.5 79 8 15
## 82 22 71 10.3 77 8 16
## 83 59 51 6.3 79 8 17
## 84 23 115 7.4 76 8 18
## 85 31 244 10.9 78 8 19
## 86 44 190 10.3 78 8 20
## 87 21 259 15.5 77 8 21
## 88 9 36 14.3 72 8 22
## 89 NA 255 12.6 75 8 23
## 90 45 212 9.7 79 8 24
## 91 168 238 3.4 81 8 25
## 92 73 215 8.0 86 8 26
## 93 NA 153 5.7 88 8 27
## 94 76 203 9.7 97 8 28
## 95 118 225 2.3 94 8 29
## 96 84 237 6.3 96 8 30
## 97 85 188 6.3 94 8 31
## 98 96 167 6.9 91 9 1
## 99 78 197 5.1 92 9 2
## 100 73 183 2.8 93 9 3
## 101 91 189 4.6 93 9 4
## 102 47 95 7.4 87 9 5
## 103 32 92 15.5 84 9 6
## 104 20 252 10.9 80 9 7
## 105 23 220 10.3 78 9 8
## 106 21 230 10.9 75 9 9
## 107 24 259 9.7 73 9 10
## 108 44 236 14.9 81 9 11
## 109 21 259 15.5 76 9 12
## 110 28 238 6.3 77 9 13
## 111 9 24 10.9 71 9 14
## 112 13 112 11.5 71 9 15
## 113 46 237 6.9 78 9 16
## 114 13 27 10.3 76 9 18
## 115 16 201 8.0 82 9 20
## 116 23 14 9.2 71 9 22
## 117 36 139 10.3 81 9 23
## 118 NA 145 13.2 77 9 27
## 119 14 191 14.3 75 9 28
## 120 18 131 8.0 76 9 29# Select the records with Temp > 80 & Month is after May
filter(airquality, Temp > 80 & Month > 5)## Ozone Solar.R Wind Temp Month Day
## 1 NA 186 9.2 84 6 4
## 2 NA 220 8.6 85 6 5
## 3 29 127 9.7 82 6 7
## 4 NA 273 6.9 87 6 8
## 5 71 291 13.8 90 6 9
## 6 39 323 11.5 87 6 10
## 7 NA 259 10.9 93 6 11
## 8 NA 250 9.2 92 6 12
## 9 23 148 8.0 82 6 13
## 10 NA 138 8.0 83 6 30
## 11 135 269 4.1 84 7 1
## 12 49 248 9.2 85 7 2
## 13 32 236 9.2 81 7 3
## 14 NA 101 10.9 84 7 4
## 15 64 175 4.6 83 7 5
## 16 40 314 10.9 83 7 6
## 17 77 276 5.1 88 7 7
## 18 97 267 6.3 92 7 8
## 19 97 272 5.7 92 7 9
## 20 85 175 7.4 89 7 10
## 21 NA 139 8.6 82 7 11
## 22 27 175 14.9 81 7 13
## 23 NA 291 14.9 91 7 14
## 24 48 260 6.9 81 7 16
## 25 35 274 10.3 82 7 17
## 26 61 285 6.3 84 7 18
## 27 79 187 5.1 87 7 19
## 28 63 220 11.5 85 7 20
## 29 NA 258 9.7 81 7 22
## 30 NA 295 11.5 82 7 23
## 31 80 294 8.6 86 7 24
## 32 108 223 8.0 85 7 25
## 33 20 81 8.6 82 7 26
## 34 52 82 12.0 86 7 27
## 35 82 213 7.4 88 7 28
## 36 50 275 7.4 86 7 29
## 37 64 253 7.4 83 7 30
## 38 59 254 9.2 81 7 31
## 39 39 83 6.9 81 8 1
## 40 9 24 13.8 81 8 2
## 41 16 77 7.4 82 8 3
## 42 78 NA 6.9 86 8 4
## 43 35 NA 7.4 85 8 5
## 44 66 NA 4.6 87 8 6
## 45 122 255 4.0 89 8 7
## 46 89 229 10.3 90 8 8
## 47 110 207 8.0 90 8 9
## 48 NA 222 8.6 92 8 10
## 49 NA 137 11.5 86 8 11
## 50 44 192 11.5 86 8 12
## 51 28 273 11.5 82 8 13
## 52 168 238 3.4 81 8 25
## 53 73 215 8.0 86 8 26
## 54 NA 153 5.7 88 8 27
## 55 76 203 9.7 97 8 28
## 56 118 225 2.3 94 8 29
## 57 84 237 6.3 96 8 30
## 58 85 188 6.3 94 8 31
## 59 96 167 6.9 91 9 1
## 60 78 197 5.1 92 9 2
## 61 73 183 2.8 93 9 3
## 62 91 189 4.6 93 9 4
## 63 47 95 7.4 87 9 5
## 64 32 92 15.5 84 9 6
## 65 44 236 14.9 81 9 11
## 66 16 201 8.0 82 9 20
## 67 36 139 10.3 81 9 23mutate(): the function is used to add new variables to the data.
mutate(airquality, TempInC = (Temp - 32) * 5 / 9)## Ozone Solar.R Wind Temp Month Day TempInC
## 1 41 190 7.4 67 5 1 19.44444
## 2 36 118 8.0 72 5 2 22.22222
## 3 12 149 12.6 74 5 3 23.33333
## 4 18 313 11.5 62 5 4 16.66667
## 5 NA NA 14.3 56 5 5 13.33333
## 6 28 NA 14.9 66 5 6 18.88889
## 7 23 299 8.6 65 5 7 18.33333
## 8 19 99 13.8 59 5 8 15.00000
## 9 8 19 20.1 61 5 9 16.11111
## 10 NA 194 8.6 69 5 10 20.55556
## 11 7 NA 6.9 74 5 11 23.33333
## 12 16 256 9.7 69 5 12 20.55556
## 13 11 290 9.2 66 5 13 18.88889
## 14 14 274 10.9 68 5 14 20.00000
## 15 18 65 13.2 58 5 15 14.44444
## 16 14 334 11.5 64 5 16 17.77778
## 17 34 307 12.0 66 5 17 18.88889
## 18 6 78 18.4 57 5 18 13.88889
## 19 30 322 11.5 68 5 19 20.00000
## 20 11 44 9.7 62 5 20 16.66667
## 21 1 8 9.7 59 5 21 15.00000
## 22 11 320 16.6 73 5 22 22.77778
## 23 4 25 9.7 61 5 23 16.11111
## 24 32 92 12.0 61 5 24 16.11111
## 25 NA 66 16.6 57 5 25 13.88889
## 26 NA 266 14.9 58 5 26 14.44444
## 27 NA NA 8.0 57 5 27 13.88889
## 28 23 13 12.0 67 5 28 19.44444
## 29 45 252 14.9 81 5 29 27.22222
## 30 115 223 5.7 79 5 30 26.11111
## 31 37 279 7.4 76 5 31 24.44444
## 32 NA 286 8.6 78 6 1 25.55556
## 33 NA 287 9.7 74 6 2 23.33333
## 34 NA 242 16.1 67 6 3 19.44444
## 35 NA 186 9.2 84 6 4 28.88889
## 36 NA 220 8.6 85 6 5 29.44444
## 37 NA 264 14.3 79 6 6 26.11111
## 38 29 127 9.7 82 6 7 27.77778
## 39 NA 273 6.9 87 6 8 30.55556
## 40 71 291 13.8 90 6 9 32.22222
## 41 39 323 11.5 87 6 10 30.55556
## 42 NA 259 10.9 93 6 11 33.88889
## 43 NA 250 9.2 92 6 12 33.33333
## 44 23 148 8.0 82 6 13 27.77778
## 45 NA 332 13.8 80 6 14 26.66667
## 46 NA 322 11.5 79 6 15 26.11111
## 47 21 191 14.9 77 6 16 25.00000
## 48 37 284 20.7 72 6 17 22.22222
## 49 20 37 9.2 65 6 18 18.33333
## 50 12 120 11.5 73 6 19 22.77778
## 51 13 137 10.3 76 6 20 24.44444
## 52 NA 150 6.3 77 6 21 25.00000
## 53 NA 59 1.7 76 6 22 24.44444
## 54 NA 91 4.6 76 6 23 24.44444
## 55 NA 250 6.3 76 6 24 24.44444
## 56 NA 135 8.0 75 6 25 23.88889
## 57 NA 127 8.0 78 6 26 25.55556
## 58 NA 47 10.3 73 6 27 22.77778
## 59 NA 98 11.5 80 6 28 26.66667
## 60 NA 31 14.9 77 6 29 25.00000
## 61 NA 138 8.0 83 6 30 28.33333
## 62 135 269 4.1 84 7 1 28.88889
## 63 49 248 9.2 85 7 2 29.44444
## 64 32 236 9.2 81 7 3 27.22222
## 65 NA 101 10.9 84 7 4 28.88889
## 66 64 175 4.6 83 7 5 28.33333
## 67 40 314 10.9 83 7 6 28.33333
## 68 77 276 5.1 88 7 7 31.11111
## 69 97 267 6.3 92 7 8 33.33333
## 70 97 272 5.7 92 7 9 33.33333
## 71 85 175 7.4 89 7 10 31.66667
## 72 NA 139 8.6 82 7 11 27.77778
## 73 10 264 14.3 73 7 12 22.77778
## 74 27 175 14.9 81 7 13 27.22222
## 75 NA 291 14.9 91 7 14 32.77778
## 76 7 48 14.3 80 7 15 26.66667
## 77 48 260 6.9 81 7 16 27.22222
## 78 35 274 10.3 82 7 17 27.77778
## 79 61 285 6.3 84 7 18 28.88889
## 80 79 187 5.1 87 7 19 30.55556
## 81 63 220 11.5 85 7 20 29.44444
## 82 16 7 6.9 74 7 21 23.33333
## 83 NA 258 9.7 81 7 22 27.22222
## 84 NA 295 11.5 82 7 23 27.77778
## 85 80 294 8.6 86 7 24 30.00000
## 86 108 223 8.0 85 7 25 29.44444
## 87 20 81 8.6 82 7 26 27.77778
## 88 52 82 12.0 86 7 27 30.00000
## 89 82 213 7.4 88 7 28 31.11111
## 90 50 275 7.4 86 7 29 30.00000
## 91 64 253 7.4 83 7 30 28.33333
## 92 59 254 9.2 81 7 31 27.22222
## 93 39 83 6.9 81 8 1 27.22222
## 94 9 24 13.8 81 8 2 27.22222
## 95 16 77 7.4 82 8 3 27.77778
## 96 78 NA 6.9 86 8 4 30.00000
## 97 35 NA 7.4 85 8 5 29.44444
## 98 66 NA 4.6 87 8 6 30.55556
## 99 122 255 4.0 89 8 7 31.66667
## 100 89 229 10.3 90 8 8 32.22222
## 101 110 207 8.0 90 8 9 32.22222
## 102 NA 222 8.6 92 8 10 33.33333
## 103 NA 137 11.5 86 8 11 30.00000
## 104 44 192 11.5 86 8 12 30.00000
## 105 28 273 11.5 82 8 13 27.77778
## 106 65 157 9.7 80 8 14 26.66667
## 107 NA 64 11.5 79 8 15 26.11111
## 108 22 71 10.3 77 8 16 25.00000
## 109 59 51 6.3 79 8 17 26.11111
## 110 23 115 7.4 76 8 18 24.44444
## 111 31 244 10.9 78 8 19 25.55556
## 112 44 190 10.3 78 8 20 25.55556
## 113 21 259 15.5 77 8 21 25.00000
## 114 9 36 14.3 72 8 22 22.22222
## 115 NA 255 12.6 75 8 23 23.88889
## 116 45 212 9.7 79 8 24 26.11111
## 117 168 238 3.4 81 8 25 27.22222
## 118 73 215 8.0 86 8 26 30.00000
## 119 NA 153 5.7 88 8 27 31.11111
## 120 76 203 9.7 97 8 28 36.11111
## 121 118 225 2.3 94 8 29 34.44444
## 122 84 237 6.3 96 8 30 35.55556
## 123 85 188 6.3 94 8 31 34.44444
## 124 96 167 6.9 91 9 1 32.77778
## 125 78 197 5.1 92 9 2 33.33333
## 126 73 183 2.8 93 9 3 33.88889
## 127 91 189 4.6 93 9 4 33.88889
## 128 47 95 7.4 87 9 5 30.55556
## 129 32 92 15.5 84 9 6 28.88889
## 130 20 252 10.9 80 9 7 26.66667
## 131 23 220 10.3 78 9 8 25.55556
## 132 21 230 10.9 75 9 9 23.88889
## 133 24 259 9.7 73 9 10 22.77778
## 134 44 236 14.9 81 9 11 27.22222
## 135 21 259 15.5 76 9 12 24.44444
## 136 28 238 6.3 77 9 13 25.00000
## 137 9 24 10.9 71 9 14 21.66667
## 138 13 112 11.5 71 9 15 21.66667
## 139 46 237 6.9 78 9 16 25.55556
## 140 18 224 13.8 67 9 17 19.44444
## 141 13 27 10.3 76 9 18 24.44444
## 142 24 238 10.3 68 9 19 20.00000
## 143 16 201 8.0 82 9 20 27.77778
## 144 13 238 12.6 64 9 21 17.77778
## 145 23 14 9.2 71 9 22 21.66667
## 146 36 139 10.3 81 9 23 27.22222
## 147 7 49 10.3 69 9 24 20.55556
## 148 14 20 16.6 63 9 25 17.22222
## 149 30 193 6.9 70 9 26 21.11111
## 150 NA 145 13.2 77 9 27 25.00000
## 151 14 191 14.3 75 9 28 23.88889
## 152 18 131 8.0 76 9 29 24.44444
## 153 20 223 11.5 68 9 30 20.00000summarise(): the function is used to summarise multiple values into a single value.
summarise(airquality, mean(Temp, na.rm = TRUE))## mean(Temp, na.rm = TRUE)
## 1 77.88235group_by(): the function is used to group data by one or more variables.
summarise(group_by(airquality, Month), mean(Temp, na.rm = TRUE))## # A tibble: 5 × 2
## Month `mean(Temp, na.rm = TRUE)`
## <int> <dbl>
## 1 5 65.5
## 2 6 79.1
## 3 7 83.9
## 4 8 84.0
## 5 9 76.9sample_n()andsample_frac(): these two functions are used to select random rows from a table.
sample_n(airquality, size = 10)## Ozone Solar.R Wind Temp Month Day
## 1 NA 295 11.5 82 7 23
## 2 97 272 5.7 92 7 9
## 3 27 175 14.9 81 7 13
## 4 NA 259 10.9 93 6 11
## 5 31 244 10.9 78 8 19
## 6 14 20 16.6 63 9 25
## 7 11 44 9.7 62 5 20
## 8 23 148 8.0 82 6 13
## 9 118 225 2.3 94 8 29
## 10 20 81 8.6 82 7 26sample_frac(airquality, size = 0.1)## Ozone Solar.R Wind Temp Month Day
## 1 97 272 5.7 92 7 9
## 2 118 225 2.3 94 8 29
## 3 71 291 13.8 90 6 9
## 4 NA 66 16.6 57 5 25
## 5 NA 153 5.7 88 8 27
## 6 84 237 6.3 96 8 30
## 7 NA 273 6.9 87 6 8
## 8 NA 259 10.9 93 6 11
## 9 44 236 14.9 81 9 11
## 10 32 92 12.0 61 5 24
## 11 14 274 10.9 68 5 14
## 12 20 252 10.9 80 9 7
## 13 NA 332 13.8 80 6 14
## 14 11 320 16.6 73 5 22
## 15 NA 255 12.6 75 8 23count(): the function tallies observations based on a group.
count(airquality, Month)## Month n
## 1 5 31
## 2 6 30
## 3 7 31
## 4 8 31
## 5 9 30arrange(): the function is used to arrange rows by variables.
arrange(airquality, desc(Month), Day)## Ozone Solar.R Wind Temp Month Day
## 1 96 167 6.9 91 9 1
## 2 78 197 5.1 92 9 2
## 3 73 183 2.8 93 9 3
## 4 91 189 4.6 93 9 4
## 5 47 95 7.4 87 9 5
## 6 32 92 15.5 84 9 6
## 7 20 252 10.9 80 9 7
## 8 23 220 10.3 78 9 8
## 9 21 230 10.9 75 9 9
## 10 24 259 9.7 73 9 10
## 11 44 236 14.9 81 9 11
## 12 21 259 15.5 76 9 12
## 13 28 238 6.3 77 9 13
## 14 9 24 10.9 71 9 14
## 15 13 112 11.5 71 9 15
## 16 46 237 6.9 78 9 16
## 17 18 224 13.8 67 9 17
## 18 13 27 10.3 76 9 18
## 19 24 238 10.3 68 9 19
## 20 16 201 8.0 82 9 20
## 21 13 238 12.6 64 9 21
## 22 23 14 9.2 71 9 22
## 23 36 139 10.3 81 9 23
## 24 7 49 10.3 69 9 24
## 25 14 20 16.6 63 9 25
## 26 30 193 6.9 70 9 26
## 27 NA 145 13.2 77 9 27
## 28 14 191 14.3 75 9 28
## 29 18 131 8.0 76 9 29
## 30 20 223 11.5 68 9 30
## 31 39 83 6.9 81 8 1
## 32 9 24 13.8 81 8 2
## 33 16 77 7.4 82 8 3
## 34 78 NA 6.9 86 8 4
## 35 35 NA 7.4 85 8 5
## 36 66 NA 4.6 87 8 6
## 37 122 255 4.0 89 8 7
## 38 89 229 10.3 90 8 8
## 39 110 207 8.0 90 8 9
## 40 NA 222 8.6 92 8 10
## 41 NA 137 11.5 86 8 11
## 42 44 192 11.5 86 8 12
## 43 28 273 11.5 82 8 13
## 44 65 157 9.7 80 8 14
## 45 NA 64 11.5 79 8 15
## 46 22 71 10.3 77 8 16
## 47 59 51 6.3 79 8 17
## 48 23 115 7.4 76 8 18
## 49 31 244 10.9 78 8 19
## 50 44 190 10.3 78 8 20
## 51 21 259 15.5 77 8 21
## 52 9 36 14.3 72 8 22
## 53 NA 255 12.6 75 8 23
## 54 45 212 9.7 79 8 24
## 55 168 238 3.4 81 8 25
## 56 73 215 8.0 86 8 26
## 57 NA 153 5.7 88 8 27
## 58 76 203 9.7 97 8 28
## 59 118 225 2.3 94 8 29
## 60 84 237 6.3 96 8 30
## 61 85 188 6.3 94 8 31
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 NA 286 8.6 78 6 1
## 94 NA 287 9.7 74 6 2
## 95 NA 242 16.1 67 6 3
## 96 NA 186 9.2 84 6 4
## 97 NA 220 8.6 85 6 5
## 98 NA 264 14.3 79 6 6
## 99 29 127 9.7 82 6 7
## 100 NA 273 6.9 87 6 8
## 101 71 291 13.8 90 6 9
## 102 39 323 11.5 87 6 10
## 103 NA 259 10.9 93 6 11
## 104 NA 250 9.2 92 6 12
## 105 23 148 8.0 82 6 13
## 106 NA 332 13.8 80 6 14
## 107 NA 322 11.5 79 6 15
## 108 21 191 14.9 77 6 16
## 109 37 284 20.7 72 6 17
## 110 20 37 9.2 65 6 18
## 111 12 120 11.5 73 6 19
## 112 13 137 10.3 76 6 20
## 113 NA 150 6.3 77 6 21
## 114 NA 59 1.7 76 6 22
## 115 NA 91 4.6 76 6 23
## 116 NA 250 6.3 76 6 24
## 117 NA 135 8.0 75 6 25
## 118 NA 127 8.0 78 6 26
## 119 NA 47 10.3 73 6 27
## 120 NA 98 11.5 80 6 28
## 121 NA 31 14.9 77 6 29
## 122 NA 138 8.0 83 6 30
## 123 41 190 7.4 67 5 1
## 124 36 118 8.0 72 5 2
## 125 12 149 12.6 74 5 3
## 126 18 313 11.5 62 5 4
## 127 NA NA 14.3 56 5 5
## 128 28 NA 14.9 66 5 6
## 129 23 299 8.6 65 5 7
## 130 19 99 13.8 59 5 8
## 131 8 19 20.1 61 5 9
## 132 NA 194 8.6 69 5 10
## 133 7 NA 6.9 74 5 11
## 134 16 256 9.7 69 5 12
## 135 11 290 9.2 66 5 13
## 136 14 274 10.9 68 5 14
## 137 18 65 13.2 58 5 15
## 138 14 334 11.5 64 5 16
## 139 34 307 12.0 66 5 17
## 140 6 78 18.4 57 5 18
## 141 30 322 11.5 68 5 19
## 142 11 44 9.7 62 5 20
## 143 1 8 9.7 59 5 21
## 144 11 320 16.6 73 5 22
## 145 4 25 9.7 61 5 23
## 146 32 92 12.0 61 5 24
## 147 NA 66 16.6 57 5 25
## 148 NA 266 14.9 58 5 26
## 149 NA NA 8.0 57 5 27
## 150 23 13 12.0 67 5 28
## 151 45 252 14.9 81 5 29
## 152 115 223 5.7 79 5 30
## 153 37 279 7.4 76 5 31Now, let’s put those commands together!
airquality %>%
filter(Temp > 70 & Month != 5) %>%
group_by(Month) %>%
summarise(mean(Temp, na.rm = TRUE))## # A tibble: 4 × 2
## Month `mean(Temp, na.rm = TRUE)`
## <int> <dbl>
## 1 6 80.0
## 2 7 83.9
## 3 8 84.0
## 4 9 79.9tidyr > tidyr is new package that makes it easy to “tidy” your data. Tidy data is data that’s easy to work with: it’s easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R’s hundreds of modelling packages).
- gather(data, key, value, …, na.rm = FALSE, convert = FALSE)
library(tidyr)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1mtcars$car <- rownames(mtcars)
mtcars <- mtcars[, c(12, 1:11)]
head(mtcars)## car mpg cyl disp hp drat wt qsec vs am
## Mazda RX4 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1
## Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1
## Datsun 710 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1
## Hornet 4 Drive Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0
## Hornet Sportabout Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0
## Valiant Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0
## gear carb
## Mazda RX4 4 4
## Mazda RX4 Wag 4 4
## Datsun 710 4 1
## Hornet 4 Drive 3 1
## Hornet Sportabout 3 2
## Valiant 3 1mtcarNew <- mtcars %>% gather(attribute, value, -car)
head(mtcarNew)## car attribute value
## 1 Mazda RX4 mpg 21.0
## 2 Mazda RX4 Wag mpg 21.0
## 3 Datsun 710 mpg 22.8
## 4 Hornet 4 Drive mpg 21.4
## 5 Hornet Sportabout mpg 18.7
## 6 Valiant mpg 18.1tail(mtcarNew)## car attribute value
## 347 Porsche 914-2 carb 2
## 348 Lotus Europa carb 2
## 349 Ford Pantera L carb 4
## 350 Ferrari Dino carb 6
## 351 Maserati Bora carb 8
## 352 Volvo 142E carb 2* spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE)mtcarSpread <- mtcarNew %>% spread(attribute, value)
head(mtcarSpread)## car am carb cyl disp drat gear hp mpg qsec vs wt
## 1 AMC Javelin 0 2 8 304 3.15 3 150 15.2 17.30 0 3.435
## 2 Cadillac Fleetwood 0 4 8 472 2.93 3 205 10.4 17.98 0 5.250
## 3 Camaro Z28 0 4 8 350 3.73 3 245 13.3 15.41 0 3.840
## 4 Chrysler Imperial 0 4 8 440 3.23 3 230 14.7 17.42 0 5.345
## 5 Datsun 710 1 1 4 108 3.85 4 93 22.8 18.61 1 2.320
## 6 Dodge Challenger 0 2 8 318 2.76 3 150 15.5 16.87 0 3.520* unite(data, col, ..., sep = "_", remove = TRUE)set.seed(1)
date <- as.Date('2016-01-01') + 0:14
hour <- sample(1:24, 15)
min <- sample(1:60, 15)
second <- sample(1:60, 15)
event <- sample(letters, 15)
data <- data.frame(date, hour, min, second, event)
data## date hour min second event
## 1 2016-01-01 4 15 35 w
## 2 2016-01-02 7 21 6 x
## 3 2016-01-03 1 37 10 f
## 4 2016-01-04 2 41 42 g
## 5 2016-01-05 11 25 38 s
## 6 2016-01-06 14 46 47 j
## 7 2016-01-07 18 58 20 y
## 8 2016-01-08 22 54 28 n
## 9 2016-01-09 5 34 54 b
## 10 2016-01-10 16 42 44 m
## 11 2016-01-11 10 56 23 r
## 12 2016-01-12 6 44 59 t
## 13 2016-01-13 19 60 40 v
## 14 2016-01-14 23 33 51 o
## 15 2016-01-15 9 20 25 adataNew <- data %>%
unite(datehour, date, hour, sep = ' ') %>%
unite(datetime, datehour, min, second, sep = ':')
dataNew## datetime event
## 1 2016-01-01 4:15:35 w
## 2 2016-01-02 7:21:6 x
## 3 2016-01-03 1:37:10 f
## 4 2016-01-04 2:41:42 g
## 5 2016-01-05 11:25:38 s
## 6 2016-01-06 14:46:47 j
## 7 2016-01-07 18:58:20 y
## 8 2016-01-08 22:54:28 n
## 9 2016-01-09 5:34:54 b
## 10 2016-01-10 16:42:44 m
## 11 2016-01-11 10:56:23 r
## 12 2016-01-12 6:44:59 t
## 13 2016-01-13 19:60:40 v
## 14 2016-01-14 23:33:51 o
## 15 2016-01-15 9:20:25 a* separate(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ...)data1 <- dataNew %>%
separate(datetime, c('date', 'time'), sep = ' ') %>%
separate(time, c('hour', 'min', 'second'), sep = ':')
data1## date hour min second event
## 1 2016-01-01 4 15 35 w
## 2 2016-01-02 7 21 6 x
## 3 2016-01-03 1 37 10 f
## 4 2016-01-04 2 41 42 g
## 5 2016-01-05 11 25 38 s
## 6 2016-01-06 14 46 47 j
## 7 2016-01-07 18 58 20 y
## 8 2016-01-08 22 54 28 n
## 9 2016-01-09 5 34 54 b
## 10 2016-01-10 16 42 44 m
## 11 2016-01-11 10 56 23 r
## 12 2016-01-12 6 44 59 t
## 13 2016-01-13 19 60 40 v
## 14 2016-01-14 23 33 51 o
## 15 2016-01-15 9 20 25 apurrr
purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. If you’ve never heard of FP before, the best place to start is the family of map() functions which allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the
map()functions is the iteration chapter in R for data science.
library(purrr)##
## Attaching package: 'purrr'## The following object is masked from 'package:plyr':
##
## compact## The following object is masked from 'package:data.table':
##
## transposemtcars %>%
split(.$cyl) %>% # from base R
map(~ lm(mpg ~ wt, data = .)) %>%
map(summary) %>%
map_dbl("r.squared")## 4 6 8
## 0.5086326 0.4645102 0.4229655stringr
stringr is built on top of stringi, which uses the ICU C library to provide fast, correct implementations of common string manipulations. stringr focusses on the most important and commonly used string manipulation functions whereas stringi provides a comprehensive set covering almost anything you can imagine.
library(stringr)
x <- c("why", "video", "cross", "extra", "deal", "authority")
str_length(x) ## [1] 3 5 5 5 4 9str_c(x, collapse = ", ")## [1] "why, video, cross, extra, deal, authority"str_sub(x, 1, 2)## [1] "wh" "vi" "cr" "ex" "de" "au"str_dup(x, 2:7)## [1] "whywhy"
## [2] "videovideovideo"
## [3] "crosscrosscrosscross"
## [4] "extraextraextraextraextra"
## [5] "dealdealdealdealdealdeal"
## [6] "authorityauthorityauthorityauthorityauthorityauthorityauthority"str_subset(x, "[aeiou]")## [1] "video" "cross" "extra" "deal" "authority"str_count(x, "[aeiou]")## [1] 0 3 1 2 2 4str_detect(x, "[aeiou]")## [1] FALSE TRUE TRUE TRUE TRUE TRUEstr_subset(x, "[aeiou]")## [1] "video" "cross" "extra" "deal" "authority"str_locate(x, "[aeiou]")## start end
## [1,] NA NA
## [2,] 2 2
## [3,] 3 3
## [4,] 1 1
## [5,] 2 2
## [6,] 1 1str_extract(x, "[aeiou]")## [1] NA "i" "o" "e" "e" "a"str_match(x, "(.)[aeiou](.)")## [,1] [,2] [,3]
## [1,] NA NA NA
## [2,] "vid" "v" "d"
## [3,] "ros" "r" "s"
## [4,] NA NA NA
## [5,] "dea" "d" "a"
## [6,] "aut" "a" "t"str_replace(x, "[aeiou]", "?")## [1] "why" "v?deo" "cr?ss" "?xtra" "d?al" "?uthority"str_split(c("a,b", "c,d,e"), ",")## [[1]]
## [1] "a" "b"
##
## [[2]]
## [1] "c" "d" "e"What’s new in R 4.1.0?
1. New pipe operator: |>
rnorm(100, mean = 4, sd = 1) |>
density() |>
plot()
c("Homo sapiens", "Mus musculus", "Rattus norvegicus") |> {function(i) grepl("homo", i, ignore.case = TRUE)}()## [1] TRUE FALSE FALSE2. Simplified function statement with \
- How did we write a self-defined function in
map()function before R 4.1.0?
map(
letters[2:3],
function(x) {
pattern <- paste0("^", x)
grep(pattern, ls("package:datasets"), value = TRUE, ignore.case = TRUE)
}
)- Since R 4.1.0, we can write it again in this style:
map(
letters[2:3],
\(x){
pattern <- paste0("^", x)
grep(pattern, ls("package:datasets"), value = TRUE, ignore.case = TRUE)
}
)## [[1]]
## [1] "beaver1" "beaver2" "BJsales" "BJsales.lead" "BOD"
##
## [[2]]
## [1] "cars" "ChickWeight" "chickwts" "co2" "CO2"
## [6] "crimtab"Updates in R 4.2.0
- Use pipe operator more elegantly with the
underscore placeholder **_**
- In R 4.1
mtcars |> (\(x) lm(hp ~ cyl, data = x))()- In R 4.2
mtcars |> lm(hp ~ cyl, data = _)Drawing graph
ggplot2 - The Art of Grammar of Graphics
ggplot2 is an R plotting system based on the Grammar of Graphics, which takes the good parts of base R and lattice graphics systems while eliminating the bad parts. It not only handles tedious plotting details (such as legend creation) but also provides a powerful graphics model that makes creating complex multi-layered graphics simple.
In-depth Analysis of Grammar of Graphics Core Philosophy
Why Do We Need a Grammar of Graphics?
In traditional graphics systems, we are usually limited to predefined chart types (scatter plots, bar charts, line plots, etc.). This is like being able to say only fixed sentences without being able to combine vocabulary to express new ideas.
# Traditional thinking: Choose fixed chart types
plot(x = mtcars$wt, y = mtcars$mpg) # Scatter plot
barplot(table(mtcars$cyl)) # Bar chart
# Grammar of Graphics thinking: Combine grammar elements
ggplot(mtcars, aes(x = wt, y = mpg)) + # Data + aesthetic mapping
geom_point() + # Geometric object
stat_smooth(method = "lm") + # Statistical transformation
facet_wrap(~ cyl) + # Faceting
theme_minimal() # ThemeSeven Core Components of Grammar of Graphics
“By understanding the grammar and how its components fit together, you can create a wider range of visualizations” - Hadley Wickham
- Data: The dataset to be visualized
- Aesthetic Mappings: How variables map to visual properties
- Layers: Composed of geometric objects and statistical transformations
- Scales: Control mapping from data values to aesthetic attributes
- Coordinate System: Define how data coordinates are displayed
- Facets: Split data into multiple subplots
- Themes: Control the overall visual appearance of the plot
Component 1: Data - The Foundation of Everything
library(ggplot2)
library(dplyr)
# Explore our data
head(mtcars)## car mpg cyl disp hp drat wt qsec vs am
## Mazda RX4 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1
## Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1
## Datsun 710 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1
## Hornet 4 Drive Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0
## Hornet Sportabout Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0
## Valiant Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0
## gear carb
## Mazda RX4 4 4
## Mazda RX4 Wag 4 4
## Datsun 710 4 1
## Hornet 4 Drive 3 1
## Hornet Sportabout 3 2
## Valiant 3 1str(mtcars)## 'data.frame': 32 obs. of 12 variables:
## $ car : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...# ggplot2's first principle: Tidy Data
# Each row is an observation, each column is a variableComponent 2: Aesthetic Mappings - Bridge Between Data and Visuals
Aesthetic mappings define how data variables correspond to visual properties. This is the core concept of Grammar of Graphics.
# Basic aesthetic mapping
p_base <- ggplot(mtcars, aes(x = wt, y = mpg))
# Global aesthetic mapping vs local aesthetic mapping
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) + # Global
geom_point() +
geom_smooth(aes(color = NULL), method = "lm", color = "black") # Local override## `geom_smooth()` using formula = 'y ~ x'
# Types of aesthetic mappings
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(
color = hp, # Continuous variable mapped to color
size = qsec, # Continuous variable mapped to size
shape = factor(am), # Categorical variable mapped to shape
alpha = vs # Continuous variable mapped to transparency
))
Aesthetic Mapping vs Setting Fixed Aesthetic Properties
# Mapping (inside aes()) vs Setting (outside aes())
p1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl))) + # Mapping: color corresponds to data
labs(title = "Mapping: Color by Cylinder Count")
p2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "red") + # Setting: all points are red
labs(title = "Setting: All Points Are Red")
# Display the difference
library(patchwork)
p1 / p2
Component 3: Layers - Building Blocks for Complex Graphics
In Grammar of Graphics, each layer contains five components:
- Data: Layer-specific data
- Aesthetic mappings: Mapping from variables to visual properties
- Geom: Type of geometric object
- Stat: Statistical transformation
- Position: Position adjustment
# Understanding layer structure
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
# Layer 1: Raw data points
geom_point(aes(color = "Raw Data"), position = position_jitter(width = 0.2)) +
# Layer 2: Statistical summary (mean)
stat_summary(fun = mean, geom = "point",
aes(color = "Mean"), size = 4, shape = 17) +
# Layer 3: Error bars
stat_summary(fun.data = mean_se, geom = "errorbar",
aes(color = "Std Error"), width = 0.2) +
# Layer 4: Trend line
geom_smooth(aes(group = 1, color = "Trend Line"), method = "lm", se = FALSE) +
scale_color_manual(values = c("Raw Data" = "gray60",
"Mean" = "red",
"Std Error" = "blue",
"Trend Line" = "darkgreen")) +
labs(title = "Multi-layer Display: Engine Cylinders vs Fuel Efficiency",
x = "Cylinders", y = "Miles Per Gallon", color = "Layer Type")## `geom_smooth()` using formula = 'y ~ x'
Relationship Between Geom and Stat
# Every geom has a corresponding default stat
# geom_bar()'s default stat is "count"
p1 <- ggplot(mtcars, aes(x = factor(cyl))) +
geom_bar() +
labs(title = "geom_bar() + stat_count()")
# stat_count()'s default geom is "bar"
p2 <- ggplot(mtcars, aes(x = factor(cyl))) +
stat_count() +
labs(title = "stat_count() + geom_bar()")
# Change default behavior
p3 <- ggplot(mtcars, aes(x = factor(cyl))) +
geom_bar(stat = "count") + # Explicitly specify
labs(title = "Explicitly Specify Stat")
p4 <- ggplot(mtcars, aes(x = factor(cyl))) +
stat_count(geom = "point", size = 4) + # Change geom
labs(title = "Display Count with Points")
(p1 | p2) / (p3 | p4)
Component 4: Scales - Bridge from Data Space to Visual Space
Scales control how data values are mapped to visual properties. Each aesthetic mapping requires a corresponding scale.
# Understanding scale function
# Continuous scales
ggplot(mtcars, aes(x = wt, y = mpg, color = hp)) +
geom_point(size = 3) +
scale_color_gradient(name = "Horsepower",
low = "blue", high = "red",
breaks = c(100, 200, 300),
labels = c("Low", "Medium", "High")) +
labs(title = "Continuous Scale: Horsepower Color Mapping")
# Discrete scales
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
scale_color_manual(name = "Cylinders",
values = c("4" = "#E69F00", "6" = "#56B4E9", "8" = "#CC79A7"),
labels = c("Four", "Six", "Eight")) +
labs(title = "Discrete Scale: Cylinder Color Mapping")
Scale Transformations and Coordinate Systems
# Logarithmic transformation
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discardggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
scale_x_log10(name = "Weight (Log Scale)",
labels = label_comma()) +
scale_y_log10(name = "MPG (Log Scale)",
labels = label_comma()) +
labs(title = "Logarithmic Scale Transformation")
# Custom breaks and labels
ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_point() +
scale_x_continuous(name = "Horsepower",
breaks = seq(50, 350, by = 50),
labels = paste0(seq(50, 350, by = 50), "HP")) +
scale_y_continuous(name = "Fuel Efficiency",
breaks = seq(10, 35, by = 5),
labels = paste0(seq(10, 35, by = 5), "mpg")) +
labs(title = "Custom Axis Labels and Breaks")
Component 5: Coordinate Systems - The Art of Positioning
Coordinate systems determine how position aesthetic attributes are mapped to the actual positions in the graphic.
# Cartesian coordinate system (default)
p1 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot() +
labs(title = "Cartesian Coordinate System")
# Flipped coordinate system
p2 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot() +
coord_flip() +
labs(title = "Flipped Coordinate System")
# Polar coordinate system
p3 <- ggplot(mtcars, aes(x = factor(cyl), fill = factor(cyl))) +
geom_bar() +
coord_polar(theta = "x") +
labs(title = "Polar Coordinate System - Pie Chart")
# Fixed ratio coordinate system
p4 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
coord_fixed(ratio = 1) +
labs(title = "Fixed Ratio Coordinate System")
(p1 | p2) / (p3 | p4)
Component 6: Facets - The Power of Small Multiples
Faceting allows splitting data into subsets, with each subset displayed in a separate panel.
# facet_wrap: one-dimensional faceting
p1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ cyl, nrow = 2,
labeller = labeller(cyl = function(x) paste(x, "Cylinders"))) +
labs(title = "facet_wrap: Faceted by Cylinder Count")
# facet_grid: two-dimensional faceting
p2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
facet_grid(am ~ cyl,
labeller = labeller(am = c("0" = "Automatic", "1" = "Manual"),
cyl = function(x) paste(x, "Cyl"))) +
labs(title = "facet_grid: Transmission Type × Cylinder Count")
p1 / p2## `geom_smooth()` using formula = 'y ~ x'
Advanced Faceting Usage
# Free scales
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ cyl, scales = "free",
labeller = labeller(cyl = function(x) paste("Cylinders:", x))) +
labs(title = "Free Scale Faceting: Each Panel Has Independent Axis Ranges",
x = "Displacement", y = "Fuel Efficiency")## `geom_smooth()` using formula = 'y ~ x'
Component 7: Themes - The Final Mile of Visual Aesthetics
Themes control the non-data elements of the plot, such as fonts, colors, and lines.
# Base plot
base_plot <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Different Theme Showcase",
subtitle = "Same Data, Different Feel",
x = "Weight (1000 lbs)",
y = "Fuel Efficiency (mpg)",
color = "Cylinders")
# Built-in theme comparison
p1 <- base_plot + theme_gray() + labs(title = "theme_gray (default)")
p2 <- base_plot + theme_minimal() + labs(title = "theme_minimal")
p3 <- base_plot + theme_classic() + labs(title = "theme_classic")
p4 <- base_plot + theme_void() + labs(title = "theme_void")
(p1 | p2) / (p3 | p4)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Custom Themes
# Create custom theme
my_theme <- theme_minimal() +
theme(
# Title settings
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray50"),
# Axis settings
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
# Legend settings
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10),
legend.position = "bottom",
# Panel settings
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "gray90", size = 0.5),
# Background settings
plot.background = element_rect(fill = "white", color = NA),
panel.background = element_rect(fill = "gray98", color = NA)
)## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Apply custom theme
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3, alpha = 0.8) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.3) +
scale_color_viridis_d(name = "Cylinders") +
labs(title = "Custom Theme Showcase",
subtitle = "Professional-Grade Visualization Design",
x = "Weight (1000 lbs)",
y = "Fuel Efficiency (mpg)") +
my_theme## `geom_smooth()` using formula = 'y ~ x'
Grammar of Graphics Practical Case: Layer-by-Layer Construction
Let’s demonstrate how to use Grammar of Graphics thinking to construct complex visualizations through a complete example.
# Step 1: Basic data layer
p <- ggplot(mtcars, aes(x = wt, y = mpg))
# Step 2: Add geometric objects
p <- p + geom_point(aes(color = factor(cyl), size = hp), alpha = 0.7)
# Step 3: Add statistical transformation
p <- p + geom_smooth(method = "lm", se = TRUE, color = "black", linetype = "dashed")
# Step 4: Customize scales
p <- p +
scale_color_manual(name = "Cylinders",
values = c("4" = "#E69F00", "6" = "#56B4E9", "8" = "#CC79A7")) +
scale_size_continuous(name = "Horsepower", range = c(2, 8),
breaks = c(100, 200, 300),
labels = c("100HP", "200HP", "300HP"))
# Step 5: Faceting
p <- p + facet_wrap(~ am, labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")))
# Step 6: Add labels
p <- p + labs(
title = "Grammar of Graphics in Practice: Car Performance Analysis",
subtitle = "Exploring Relationships Between Weight, Fuel Efficiency, Power, and Transmission",
x = "Weight (1000 lbs)",
y = "Fuel Efficiency (mpg)",
caption = "Data Source: mtcars dataset"
)
# Step 7: Apply theme
p <- p + theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5, color = "gray50"),
strip.text = element_text(size = 11, face = "bold"),
legend.position = "bottom"
)
print(p)## `geom_smooth()` using formula = 'y ~ x'
Advanced Grammar Concepts: Position Adjustments
Position adjustments solve the problem of overlapping geometric objects and are an important concept in Grammar of Graphics.
# Prepare data
df <- data.frame(
category = rep(c("A", "B", "C"), each = 100),
value = c(rnorm(100, 5, 1), rnorm(100, 7, 1.5), rnorm(100, 6, 2)),
group = rep(c("Group1", "Group2"), 150)
)
# Different position adjustments
p1 <- ggplot(df, aes(x = category, y = value, fill = group)) +
geom_col(position = "stack") +
labs(title = "position = 'stack'")
p2 <- ggplot(df, aes(x = category, y = value, fill = group)) +
geom_col(position = "dodge") +
labs(title = "position = 'dodge'")
p3 <- ggplot(df, aes(x = category, y = value, fill = group)) +
geom_col(position = "fill") +
labs(title = "position = 'fill'")
p4 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_point(position = position_jitter(width = 0.3, height = 0)) +
labs(title = "position_jitter")
(p1 | p2) / (p3 | p4)
Complete Grammar of Graphics Workflow
# Simulate a data science project visualization workflow
# 1. Data preparation
mtcars_extended <- mtcars %>%
mutate(
efficiency_category = case_when(
mpg < 15 ~ "Low Efficiency",
mpg < 25 ~ "Medium Efficiency",
TRUE ~ "High Efficiency"
),
weight_category = case_when(
wt < 2.5 ~ "Light",
wt < 4 ~ "Medium",
TRUE ~ "Heavy"
)
)
# 2. Exploratory visualization: Using Grammar of Graphics
final_plot <- ggplot(mtcars_extended,
aes(x = wt, y = mpg)) +
# Data layer 1: Background trend
geom_smooth(method = "loess", se = TRUE, alpha = 0.2, color = "gray50") +
# Data layer 2: Main data points
geom_point(aes(color = efficiency_category,
size = hp,
shape = factor(am)),
alpha = 0.8) +
# Data layer 3: Category trend lines
geom_smooth(aes(color = efficiency_category),
method = "lm", se = FALSE, size = 1.2) +
# Scale settings
scale_color_manual(name = "Efficiency Level",
values = c("Low Efficiency" = "#d73027",
"Medium Efficiency" = "#fee08b",
"High Efficiency" = "#1a9850")) +
scale_size_continuous(name = "Horsepower", range = c(2, 8)) +
scale_shape_manual(name = "Transmission",
values = c("0" = 16, "1" = 17),
labels = c("Automatic", "Manual")) +
# Faceting
facet_wrap(~ weight_category, scales = "free_x",
labeller = labeller(weight_category = function(x) paste("Weight Category:", x))) +
# Labels
labs(
title = "Comprehensive Car Performance Analysis",
subtitle = "Using Grammar of Graphics to Explore Complex Relationships Between Weight, Fuel Efficiency, Power, and Transmission",
x = "Vehicle Weight (1000 lbs)",
y = "Fuel Efficiency (miles per gallon)",
caption = "Data visualization demonstrates the combined power of Grammar of Graphics\nEach visual element carries specific information"
) +
# Theme
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray40"),
plot.caption = element_text(size = 9, color = "gray50", hjust = 1),
strip.text = element_text(size = 11, face = "bold"),
legend.position = "bottom",
legend.box = "horizontal",
panel.grid.minor = element_blank()
) +
guides(
color = guide_legend(override.aes = list(size = 4)),
size = guide_legend(override.aes = list(alpha = 1)),
shape = guide_legend(override.aes = list(size = 4))
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.print(final_plot)## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 4.0632## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.2818## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 0.032678## Warning in sqrt(sum.squares/one.delta): NaNs produced## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : span too small. fewer
## data values than degrees of freedom.## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 4.0632## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.2818## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 0.032678## Warning in stats::qt(level/2 + 0.5, pred$df): NaNs produced## `geom_smooth()` using formula = 'y ~ x'## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
Grammar of Graphics Learning Key Points Summary
- Mindset Shift: From “choosing chart types” to “combining grammar elements”
- Layering Concept: Each layer has a clear purpose and function
- Mapping vs Setting: Understanding when to use
aes()and when not to - Scale Importance: Each aesthetic mapping requires a corresponding scale
- Iterative Improvement: Grammar of Graphics supports gradual plot refinement
Basic Syntax Review and Hands-on Practice
After gaining a deep understanding of the theoretical foundation of Grammar of Graphics, let’s review the basic syntax structure:
# ggplot2 basic syntax structure
ggplot(data = <DATA>) + # 1. Specify data
<GEOM_FUNCTION>( # 2. Choose geometric object
mapping = aes(<MAPPINGS>), # 3. Define aesthetic mapping
stat = <STAT>, # 4. Statistical transformation (optional)
position = <POSITION> # 5. Position adjustment (optional)
) +
<SCALE_FUNCTION>() + # 6. Custom scales (optional)
<COORDINATE_FUNCTION>() + # 7. Coordinate system (optional)
<FACET_FUNCTION>() + # 8. Faceting (optional)
<THEME_FUNCTION>() # 9. Theme (optional)Quick Reference for Common Geometric Objects
Common geom cases for one variable
Common geom cases for two variables
Hands-on Practice: From Basic to Advanced
Practice 1: Progressive Construction of Scatter Plots
Let’s use Grammar of Graphics thinking to progressively build a scatter plot:
# Step 1: Only specify data and basic mapping
p1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
labs(title = "Step 1: Basic Scatter Plot")
# Step 2: Add color mapping
p2 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl))) +
labs(title = "Step 2: Add Color")
# Step 3: Add size mapping
p3 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl), size = hp)) +
labs(title = "Step 3: Add Size")
# Step 4: Add shape and transparency
p4 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl), size = hp, shape = factor(am)), alpha = 0.7) +
labs(title = "Step 4: Multiple Aesthetic Mappings")
(p1 | p2) / (p3 | p4)
Practice 2: Applying Grammar Components
# Apply all the components we've learned
ggplot(mtcars, aes(x = wt, y = mpg)) +
# Layer 1: Scatter plot
geom_point(aes(color = factor(cyl), size = hp), alpha = 0.6) +
# Layer 2: Trend line
geom_smooth(method = "lm", se = TRUE, color = "black") +
# Scale settings
scale_color_brewer(name = "Cylinders", type = "qual", palette = "Set1") +
scale_size_continuous(name = "Horsepower", range = c(2, 6)) +
# Coordinate system
coord_cartesian(xlim = c(1, 6), ylim = c(10, 35)) +
# Labels
labs(
title = "Grammar of Graphics Practice",
subtitle = "Composite Plot Integrating Multiple Components",
x = "Weight (1000 lbs)",
y = "Fuel Efficiency (mpg)",
caption = "Demonstrates integration of data, aesthetic mappings, layers, scales, and coordinate systems"
) +
# Theme
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "bottom"
)## `geom_smooth()` using formula = 'y ~ x'
In-depth Exploration of Common Geometric Objects
After understanding the theoretical foundation of Grammar of Graphics, let’s explore the practical applications of various geometric objects:
Line Plots
# Line plot using economics dataset
ggplot(data = economics) +
geom_line(mapping = aes(x = date, y = unemploy))
Bar Charts
# Bar chart of car counts by cylinder
ggplot(data = mtcars) +
geom_bar(mapping = aes(x = factor(cyl), fill = factor(cyl)))
Histograms
# Histogram of mpg distribution
ggplot(data = mtcars) +
geom_histogram(mapping = aes(x = mpg), bins = 10, fill = "skyblue", color = "black")
Box Plots
# Box plot of mpg by cylinder
ggplot(data = mtcars) +
geom_boxplot(mapping = aes(x = factor(cyl), y = mpg, fill = factor(cyl)))
Statistical Transformations
# Adding smooth trend line
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
# Statistical summary
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg)) +
geom_point(position = "jitter", alpha = 0.6) +
stat_summary(fun = mean, geom = "point", color = "red", size = 3) +
stat_summary(fun.data = mean_se, geom = "errorbar", color = "red", width = 0.2)
Coordinate Systems and Scales
# Coordinate transformation
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
coord_flip() # Flip x and y axes
# Custom scales
ggplot(data = mtcars, aes(x = wt, y = mpg, color = hp)) +
geom_point(size = 3) +
scale_color_gradient(low = "blue", high = "red") +
scale_x_continuous(name = "Weight (1000 lbs)") +
scale_y_continuous(name = "Miles per Gallon")
Faceting (Multiple Panels)
# Facet wrap
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
facet_wrap(~ cyl, nrow = 2)
# Facet grid
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
facet_grid(am ~ cyl, labeller = label_both)
Customizing Themes and Appearance
# Using built-in themes
ggplot(data = mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
theme_minimal()
# Custom theme modifications
ggplot(data = mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
labs(title = "Car Weight vs Fuel Efficiency",
subtitle = "Relationship between weight and MPG by cylinder count",
x = "Weight (1000 lbs)",
y = "Miles per Gallon",
color = "Cylinders",
caption = "Data source: mtcars dataset") +
theme_classic() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray50"),
legend.position = "bottom",
panel.grid.major = element_line(color = "gray90", size = 0.5)
)
Advanced Customization Techniques
Creating Professional Publication-Ready Plots
# Professional-looking plot with multiple layers
p <- ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl), size = hp), alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "black", linetype = "dashed") +
scale_color_manual(values = c("4" = "#E69F00", "6" = "#56B4E9", "8" = "#CC79A7"),
name = "Cylinders") +
scale_size_continuous(name = "Horsepower", range = c(2, 6)) +
labs(
title = "Relationship Between Car Weight and Fuel Efficiency",
subtitle = "Data points colored by cylinder count and sized by horsepower",
x = "Weight (1000 lbs)",
y = "Miles per Gallon (MPG)",
caption = "Source: Motor Trend Car Road Tests (mtcars dataset)"
) +
theme_bw() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5, color = "gray40"),
plot.caption = element_text(size = 9, color = "gray50"),
legend.position = "right",
legend.box = "vertical",
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = "gray90")
)
print(p)## `geom_smooth()` using formula = 'y ~ x'
Combining Multiple Plots
# Using patchwork package for combining plots (install if needed)
# install.packages("patchwork")
library(patchwork)
# Create individual plots
p1 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot(fill = "lightblue") +
labs(title = "MPG by Cylinders", x = "Cylinders", y = "MPG")
p2 <- ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_point(color = "red") +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "MPG vs Horsepower", x = "Horsepower", y = "MPG")
p3 <- ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 10, fill = "green", alpha = 0.7) +
labs(title = "MPG Distribution", x = "MPG", y = "Count")
# Combine plots
(p1 | p2) / p3## `geom_smooth()` using formula = 'y ~ x'
Practical Tips for Effective Plotting
- Start Simple: Begin with basic plots and add complexity gradually
- Choose Appropriate Geoms: Match the geometry to your data type
- Use Color Wisely: Ensure accessibility with colorblind-friendly palettes
- Label Everything: Always include informative titles, axis labels, and legends
- Maintain Consistency: Use consistent styling across related plots
- Consider Your Audience: Adjust complexity based on who will view the plot
Useful ggplot2 Extensions
# Popular ggplot2 extension packages
install.packages(c("ggthemes", "viridis", "plotly", "gganimate"))
# Example with ggthemes
library(ggthemes)
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
theme_economist() +
scale_color_economist()Resources for Further Learning
- 林茂廷老師的ggplot2介紹
- ggplot2 Official Documentation
- R Graphics Cookbook
- ggplot2 Cheat Sheet
- Edwin Chen’s ggplot2 tutorial
Version control
Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later. For the examples in this book you will use software source code as the files being version controlled, though in reality you can do this with nearly any type of file on a computer. –from git
Installing git
- ubuntu users:
sudo apt-get install git - RedHat/Fedora/CentOS users:
sudo yum install git - Mac users: http://git-scm.com/download/mac
- Windows users: http://git-scm.com/download/win
Git setup
git config --global user.name "YOUR NAME"
git config --global user.email you.email@address.org
git config --global core.ui true
git config --global core.editor vim
# For windows users
git config --global core.quotepath offGit basics
## Initializing a repository in an existing directory
# Go to the project's directory and type
git init
# Add files you want to track
git add LICENSE
git add READ.md
git commit -m 'First commit. Add LICENSE & READ.md'
# Add new files
git add R.Rmd
git add helloworld.r
git commit -m 'Second commit. Add R.Rmd, helloworld.r'
git remote add origin
git push -u origin master
# Recover your codes to the last commit
git checkout -- filename
git reset --hard
## Cloning an existing repository
git clone https://github.com/godkin1211/Rcourses.git
git pull https://github.com/godkin1211/Rcourses.git